The prospecting for underground oil and gas reservoirs is often performed by the use of seismic vibrations and waves which are intentionally input into the earth at a source location, and which are detected at remote locations by geophones (in the case of prospecting on land) or hydrophones (for offshore prospecting) The travel times of vibrations from the source to the detection locations is indicative of the depth of various geological features such as interfaces between sub-surface strata, and the presence of hydrocarbon reservoirs thereat, from which the seismic waves reflect.
Distortions in the waves during transmission through the earth makes the recognition of the travel times more difficult. Particularly in land-based prospecting, a significant source of distortion is the near-surface layer. The near-surface distortion is due in large part to the weathering of the material in this layer; the soil and rocks near the surface are affected by ice, frost, water, temperature and wind to a greater degree than are the soil and rocks at greater depths from the surface. Accordingly, the near-surface layer distorts seismic waves and vibrations traveling therethrough, with the weathering effects described above presenting distortion due to lateral changes in velocity within this layer, lateral changes in the thickness of this layer, or both.
In order to accurately determine the travel time of the deep reflected waves, correction for this distortion caused by the near-surface layers must be done. A common technique for such correction uses a measurement of the velocity of refracted waves in the near-surface layer (i.e., traveling directly from the source to the detection locations along the near-surface strata, without reflecting from deeper sub-surface strata). The measured travel times of these refracted waves can be used to define a near-surface velocity model by conventional techniques. This velocity model is then used to perform conventional static corrections to the measured vibrational waves from deep reflections. These static corrections will account for distortion from the weathered near-surface layer, and will improve the accuracy of the seismic survey.
Such correction for the near-surface effects is conventionally performed by analysis of seismic traces from the surface geophones, in the land-based example. These conventional seismic traces are generally manually analyzed to identify the so-called "first break" point for each trace. These first breaks in the traces correspond to the first detection by the geophones of the source wave, and accordingly may be used to determine the velocity of the refracted waves through the near surface layer.
However, identification of the first break in a trace is not always straightforward, due to the distortion in the weathered near-surface layer as described above. Secondly, manual analysis of large numbers of traces (on the order of 100 traces for each seismic shot record, and tens or hundreds of shot records in a seismic survey), is heavily labor intensive. While certain redundancies are present in the traces which could be utilized to correct erroneous first picks, for example by creating and analyzing groups of traces (or "gathers") with common offset or common receiver, the heavy labor required for the primary analysis precludes exploitation of the redundancies available in the data.
Clearly, due to the large amount of data, automation of the first break analysis is quite desirable. Various techniques have been proposed for the automation of first break picking from, or other analysis of, large numbers of seismic traces. Examples of these techniques are described in Ervin et al., "Automated analysis of marine refraction data: A computer algorithm", Geophysics, Vol. 48, No. 5 (May 1983), pp. 582-589; and Gelchinsky et al., "Automatic Picking of First Arrivals and Parameterization of Traveltime Curves", Geophysical Prospecting 31 (1983), pp. 915-928. These prior techniques have been based on statistical and mathematical features of the seismic signals such as running averages of the trace slope and statistical treatment of the signals' correlation properties. However, due to variations in the quality of the seismic signals along the seismic profile and to other factors, the performance of these prior techniques is questionable.
It is therefore an object of this invention to provide a method and apparatus for automated first break picking which uses an adaptive computer network such as a neural network.
Neural networks refer to a class of computations that can be implemented in computing hardware, or more frequently computer programs implemented on conventional computing hardware, organized according to what is currently believed to be the architecture of biological neurological systems. The distinguishing feature of neural networks is that they are arranged into a network of elements, mimicking neurodes of a human brain. In such networks, each element performs a relatively simple calculation, such as a weighted sum of its inputs applied to a non-linear function, such as a sigmoid, to determine the state of the output. Increased power of computation comes from having a large number of such elements interconnected to one another, resulting in a network having both parallel and sequential arrangements of computational elements. Proper setting of the weighting factors for each of the elements allows the network to perform complex functions such as image recognition, solving optimization problems, and the like.
The programming of conventional computer systems to operate as an artificial neural network is well known in the art, as described in Y. H. Pao, Adaptive Pattern Recognition and Neural Networks, (Addison-Wesley Publishing Company, New York, 1989), incorporated herein by this reference. As described therein, such programming can be done in high level languages such as C.
A particular type of neural network which is of interest is referred to as the backpropagation network. Such a network generally includes multiple layers of elements as described above. Adaptation of the network to a particular task is done by way of "training" the network with a number of examples, setting the weighting factors for each element to the proper value. This training is accomplished by presenting inputs to the network, analyzing the output of the network, and adjusting the weighting factors according to the difference between the actual output and the desired output for the training example. Upon sufficient training, the network is adapted to respond to new inputs (for which the answer is not known a priori), by generating an output which is similar to the result which a human expert would present for the same inputs. An example of a conventional backpropagation algorithm for training a neural network of this type is described in Rumelhart et al., Parallel Distributed Processing (The MIT Press, Cambridge, Mass., 1988), incorporated herein by this reference.
In such backpropagation neural networks, certain limitations exist which, in turn, limit the usefulness of neural networks in addressing the problem of first break identification. A first limitation is that the learning rates of conventional neural networks is quite slow. With the number of training cycles often numbering into the tens and hundreds of thousands for moderately complex problems, the usefulness of a neural network trained according to conventional methods and used in an interactive mode, such as is faced in the first break picking application described hereinabove, is quite limited.
Secondly, a serious problem for backpropagation neural networks is the possibility that the training may reach a state commonly referred to as a slow convergence region. A first example of a slow convergence region is trapping in a local minimum, where incremental change of the weighting factors in any direction increases the error, but where the network error is not at its lowest, or global, minimum. Another example of a slow convergence region is a region where the error gradient is exceedingly low (i.e., change in the weighting factors reduces the error only by an insignificant amount). Such local minimum trapping, low error gradient regions, and the like will be hereinafter referred to cumulatively as "slow convergence regions". Since conventional training methods rely on improvement of the result (i.e., reduction in an error term), one or more slow convergence regions may exist for a problem, in which the training of the network becomes slow, at best, or converges to an incorrect result.
Other inefficiencies also are present in conventional training methods which relate to the speed at which the set of weighting factors converge at the desired result, as will be described in further detail hereinbelow.
It is therefore a further object of this invention to provide a neural network which is adapted to perform recognition of first breaks in seismic traces.
It is a further object of this invention to provide a neural network which performs such recognition of first breaks using both information from the trace under analysis and also information from neighboring traces.
It is a further object of this invention to provide a method of training an adaptive or neural network in a more efficient manner.
It is a further object of this invention to provide a method of training an adaptive or neural network which is less susceptible to convergence problems in a slow convergence region, and which accordingly trains the network more efficiently.
It is a further object of this invention to provide a method of training a network which includes techniques for escaping from slow convergence regions.
Other objects and advantages of the invention will be apparent to those of ordinary skill in the art having reference to the following specification in combination with the drawings.